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MWirelabs
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Model Details
Model Provider
MWirelabs
Model Tree
google/gemma-3-1b-it
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch # Load model and tokenizertokenizer = AutoTokenizer.from_pretrained("MWirelabs/neodac-mini")model = AutoModelForCausalLM.from_pretrained( "MWirelabs/neodac-mini", torch_dtype=torch.bfloat16, device_map="auto") # Example usagedef ask_neodac-mini(question): prompt = f"<start_of_turn>user\n{question}<end_of_turn>\n<start_of_turn>model\n" inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( **inputs, max_length=300, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response.split("<start_of_turn>model\n")[-1].strip() # Ask about Northeast India cultureresponse = ask_neodac-mini("What is the significance of bamboo in Northeast India?")print(response)| Aspect | Base Gemma 3 1B-IT | Neodac-mini |
|---|---|---|
| Cultural Accuracy | ❌ Hallucinations | ✅ Factually correct |
| Response Detail | ⚠️ Generic/brief | ✅ Rich & comprehensive |
| Regional Context | ❌ Limited knowledge | ✅ Deep cultural understanding |
| Tribal Information | ❌ Inaccurate/missing | ✅ Authentic representation |
Base Model Response:
Claims Bihu is about Lord Shiva (incorrect)
Neodac-mini Response:
Bihu is the most important festival of Assam, celebrated by all Assamese people. There are three Bihus that mark different stages of the agricultural calendar: Rongali (or Bohag) Bihu in spring, Kati (or Kongali) Bihu in autumn, and Magh (or Bhogali) Bihu in winter.
The model was evaluated on cultural accuracy, response completeness, and factual correctness. Significant improvements were observed over the base model in all cultural domains.
If you use Neodac-mini in your research or applications, please cite:
bibtex
@misc{neodac2025, title={Neodac-mini: A Specialized Language Model for Northeast India Cultural Knowledge}, author={MWire Labs}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/MWirelabs/neodac-mini}, note={Fine-tuned from google/gemma-3-1b-it for cultural preservation and education}}Interested in improving Neodac-mini? We welcome:
This model is released under the Apache 2.0 license, same as the base Gemma model.
Neodac-mini represents a step forward in culturally-aware AI, preserving and making accessible the rich heritage of Northeast India through technology.
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch # Load model and tokenizertokenizer = AutoTokenizer.from_pretrained("MWirelabs/neodac-mini")model = AutoModelForCausalLM.from_pretrained( "MWirelabs/neodac-mini", torch_dtype=torch.bfloat16, device_map="auto") # Example usagedef ask_neodac-mini(question): prompt = f"<start_of_turn>user\n{question}<end_of_turn>\n<start_of_turn>model\n" inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( **inputs, max_length=300, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response.split("<start_of_turn>model\n")[-1].strip() # Ask about Northeast India cultureresponse = ask_neodac-mini("What is the significance of bamboo in Northeast India?")print(response)@misc{neodac2025, title={Neodac-mini: A Specialized Language Model for Northeast India Cultural Knowledge}, author={MWire Labs}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/MWirelabs/neodac-mini}, note={Fine-tuned from google/gemma-3-1b-it for cultural preservation and education}}